如何构建一个双编码器神经网络模型
如何构建一个双编码器（也称为双塔）神经网络模型，以使用自然语言搜索图像。
1 介绍 该示例演示了如何构建一个双编码器（也称为双塔）神经网络模型，以使用自然语言搜索图像。该模型的灵感来自于alec radford等人提出的clip方法，其思想是联合训练一个视觉编码器和一个文本编码器，将图像及其标题的表示投射到同一个嵌入空间，从而使标题嵌入位于其描述的图像的嵌入附近。 这个例子需要tensorflow 2.4或更高版本。此外，bert模型需要tensorflow hub和tensorflow text，adamw优化器需要tensorflow addons。这些库可以使用以下命令进行安装。
pip install -q -u tensorflow-hub tensorflow-text tensorflow-addons
2 安装
import osimport collectionsimport jsonimport numpy as npimport tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import layersimport tensorflow_hub as hubimport tensorflow_text as textimport tensorflow_addons as tfaimport matplotlib.pyplot as pltimport matplotlib.image as mpimgfrom tqdm import tqdm # suppressing tf.hub warningstf.get_logger().setlevel(error)
3 准备数据
我们使用ms-coco数据集来训练我们的双编码器模型。ms-coco包含超过82,000张图片，每张图片至少有5个不同的标题注释。该数据集通常用image captioning任务，但我们可以重新利用图像标题对来训练双编码器模型进行图像搜索。
下载提取数据
首先，下载数据集，它由两个压缩文件夹组成：一个是图像，另一个是相关的图像标题。值得注意的是压缩后的图像文件夹大小为13gb。
root_dir = datasetsannotations_dir = os.path.join(root_dir, annotations)images_dir = os.path.join(root_dir, train2014)tfrecords_dir = os.path.join(root_dir, tfrecords)annotation_file = os.path.join(annotations_dir, captions_train2014.json) # download caption annotation filesif not os.path.exists(annotations_dir): annotation_zip = tf.keras.utils.get_file( captions.zip, cache_dir=os.path.abspath(.), origin=https://images.cocodataset.org/annotations/annotations_trainval2014.zip, extract=true, ) os.remove(annotation_zip) # download image filesif not os.path.exists(images_dir): image_zip = tf.keras.utils.get_file( train2014.zip, cache_dir=os.path.abspath(.), origin=https://images.cocodataset.org/zips/train2014.zip, extract=true, ) os.remove(image_zip) print(dataset is downloaded and extracted successfully.) with open(annotation_file, r) as f: annotations = json.load(f)[annotations] image_path_to_caption = collections.defaultdict(list)for element in annotations: caption = f{element['caption'].lower().rstrip('.')} image_path = images_dir + /coco_train2014_ + %012d.jpg % (element[image_id]) image_path_to_caption[image_path].append(caption) image_paths = list(image_path_to_caption.keys())print(fnumber of images: {len(image_paths)})
downloading data from https://images.cocodataset.org/annotations/annotations_trainval2014.zip252878848/252872794 [==============================] - 5s 0us/stepdownloading data from https://images.cocodataset.org/zips/train2014.zip13510574080/13510573713 [==============================] - 394s 0us/stepdataset is downloaded and extracted successfully.number of images: 82783
处理并将数据保存到tfrecord文件中
你可以改变sample_size参数去控制将用于训练双编码器模型的多对图像-标题。在这个例子中，我们将training_size设置为30000张图像，约占数据集的35%。我们为每张图像使用2个标题，从而产生60000个图像-标题对。训练集的大小会影响生成编码器的质量，样本越多，训练时间越长。
train_size = 30000valid_size = 5000captions_per_image = 2images_per_file = 2000train_image_paths = image_paths[:train_size]num_train_files = int(np.ceil(train_size / images_per_file))train_files_prefix = os.path.join(tfrecords_dir, train) valid_image_paths = image_paths[-valid_size:]num_valid_files = int(np.ceil(valid_size / images_per_file))valid_files_prefix = os.path.join(tfrecords_dir, valid) tf.io.gfile.makedirs(tfrecords_dir) def bytes_feature(value): return tf.train.feature(bytes_list=tf.train.byteslist(value=[value])) def create_example(image_path, caption): feature = { caption: bytes_feature(caption.encode()), raw_image: bytes_feature(tf.io.read_file(image_path).numpy()), } return tf.train.example(features=tf.train.features(feature=feature)) def write_tfrecords(file_name, image_paths): caption_list = [] image_path_list = [] for image_path in image_paths: captions = image_path_to_caption[image_path][:captions_per_image] caption_list.extend(captions) image_path_list.extend([image_path] * len(captions)) with tf.io.tfrecordwriter(file_name) as writer: for example_idx in range(len(image_path_list)): example = create_example( image_path_list[example_idx], caption_list[example_idx] ) writer.write(example.serializetostring()) return example_idx + 1 def write_data(image_paths, num_files, files_prefix): example_counter = 0 for file_idx in tqdm(range(num_files)): file_name = files_prefix + -%02d.tfrecord % (file_idx) start_idx = images_per_file * file_idx end_idx = start_idx + images_per_file example_counter += write_tfrecords(file_name, image_paths[start_idx:end_idx]) return example_counter train_example_count = write_data(train_image_paths, num_train_files, train_files_prefix)print(f{train_example_count} training examples were written to tfrecord files.) valid_example_count = write_data(valid_image_paths, num_valid_files, valid_files_prefix)print(f{valid_example_count} evaluation examples were written to tfrecord files.)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [03:19<00:00, 13.27s/it] 0%| | 0/3 [00:00 创建用于训练和评估的 tf.data.dataset
feature_description = { caption: tf.io.fixedlenfeature([], tf.string), raw_image: tf.io.fixedlenfeature([], tf.string),} def read_example(example): features = tf.io.parse_single_example(example, feature_description) raw_image = features.pop(raw_image) features[image] = tf.image.resize( tf.image.decode_jpeg(raw_image, channels=3), size=(299, 299) ) return features def get_dataset(file_pattern, batch_size): return ( tf.data.tfrecorddataset(tf.data.dataset.list_files(file_pattern)) .map( read_example, num_parallel_calls=tf.data.experimental.autotune, deterministic=false, ) .shuffle(batch_size * 10) .prefetch(buffer_size=tf.data.experimental.autotune) .batch(batch_size) ) 4 实时投影头
投影头用于将图像和文字嵌入到具有相同的维度的同一嵌入空间。
def project_embeddings( embeddings, num_projection_layers, projection_dims, dropout_rate): projected_embeddings = layers.dense(units=projection_dims)(embeddings) for _ in range(num_projection_layers): x = tf.nn.gelu(projected_embeddings) x = layers.dense(projection_dims)(x) x = layers.dropout(dropout_rate)(x) x = layers.add()([projected_embeddings, x]) projected_embeddings = layers.layernormalization()(x) return projected_embeddings 5 实现视觉编码器
在本例中，我们使用keras applications的xception作为视觉编码器的基础。
def create_vision_encoder( num_projection_layers, projection_dims, dropout_rate, trainable=false): # load the pre-trained xception model to be used as the base encoder. xception = keras.applications.xception( include_top=false, weights=imagenet, pooling=avg ) # set the trainability of the base encoder. for layer in xception.layers: layer.trainable = trainable # receive the images as inputs. inputs = layers.input(shape=(299, 299, 3), name=image_input) # preprocess the input image. xception_input = tf.keras.applications.xception.preprocess_input(inputs) # generate the embeddings for the images using the xception model. embeddings = xception(xception_input) # project the embeddings produced by the model. outputs = project_embeddings( embeddings, num_projection_layers, projection_dims, dropout_rate ) # create the vision encoder model. return keras.model(inputs, outputs, name=vision_encoder) 6 实现文本编码器 我们使用tensorflow hub的bert作为文本编码器
def create_text_encoder( num_projection_layers, projection_dims, dropout_rate, trainable=false): # load the bert preprocessing module. preprocess = hub.keraslayer( https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/2, name=text_preprocessing, ) # load the pre-trained bert model to be used as the base encoder. bert = hub.keraslayer( https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_l-4_h-512_a-8/1, bert, ) # set the trainability of the base encoder. bert.trainable = trainable # receive the text as inputs. inputs = layers.input(shape=(), dtype=tf.string, name=text_input) # preprocess the text. bert_inputs = preprocess(inputs) # generate embeddings for the preprocessed text using the bert model. embeddings = bert(bert_inputs)[pooled_output] # project the embeddings produced by the model. outputs = project_embeddings( embeddings, num_projection_layers, projection_dims, dropout_rate ) # create the text encoder model. return keras.model(inputs, outputs, name=text_encoder)
7 实现双编码器
为了计算loss，我们计算每个 caption_i和 images_j之间的对偶点积相似度作为预测值。caption_i和image_j之间的目标相似度计算为（caption_i和caption_j之间的点积相似度）和（image_i和image_j之间的点积相似度）的平均值。然后，我们使用交叉熵来计算目标和预测之间的损失。
class dualencoder(keras.model): def __init__(self, text_encoder, image_encoder, temperature=1.0, **kwargs): super(dualencoder, self).__init__(**kwargs) self.text_encoder = text_encoder self.image_encoder = image_encoder self.temperature = temperature self.loss_tracker = keras.metrics.mean(name=loss) @property def metrics(self): return [self.loss_tracker] def call(self, features, training=false): # place each encoder on a separate gpu (if available). # tf will fallback on available devices if there are fewer than 2 gpus. with tf.device(/gpu:0): # get the embeddings for the captions. caption_embeddings = text_encoder(features[caption], training=training) with tf.device(/gpu:1): # get the embeddings for the images. image_embeddings = vision_encoder(features[image], training=training) return caption_embeddings, image_embeddings def compute_loss(self, caption_embeddings, image_embeddings): # logits[i][j] is the dot_similarity(caption_i, image_j). logits = ( tf.matmul(caption_embeddings, image_embeddings, transpose_b=true) / self.temperature ) # images_similarity[i][j] is the dot_similarity(image_i, image_j). images_similarity = tf.matmul( image_embeddings, image_embeddings, transpose_b=true ) # captions_similarity[i][j] is the dot_similarity(caption_i, caption_j). captions_similarity = tf.matmul( caption_embeddings, caption_embeddings, transpose_b=true ) # targets[i][j] = avarage dot_similarity(caption_i, caption_j) and dot_similarity(image_i, image_j). targets = keras.activations.softmax( (captions_similarity + images_similarity) / (2 * self.temperature) ) # compute the loss for the captions using crossentropy captions_loss = keras.losses.categorical_crossentropy( y_true=targets, y_pred=logits, from_logits=true ) # compute the loss for the images using crossentropy images_loss = keras.losses.categorical_crossentropy( y_true=tf.transpose(targets), y_pred=tf.transpose(logits), from_logits=true ) # return the mean of the loss over the batch. return (captions_loss + images_loss) / 2 def train_step(self, features): with tf.gradienttape() as tape: # forward pass caption_embeddings, image_embeddings = self(features, training=true) loss = self.compute_loss(caption_embeddings, image_embeddings) # backward pass gradients = tape.gradient(loss, self.trainable_variables) self.optimizer.apply_gradients(zip(gradients, self.trainable_variables)) # monitor loss self.loss_tracker.update_state(loss) return {loss: self.loss_tracker.result()} def test_step(self, features): caption_embeddings, image_embeddings = self(features, training=false) loss = self.compute_loss(caption_embeddings, image_embeddings) self.loss_tracker.update_state(loss) return {loss: self.loss_tracker.result()}
8 训练双编码模型
在这个实验中，我们冻结了文字和图像的基础编码器，只让投影头进行训练。
num_epochs = 5 # in practice, train for at least 30 epochsbatch_size = 256 vision_encoder = create_vision_encoder( num_projection_layers=1, projection_dims=256, dropout_rate=0.1)text_encoder = create_text_encoder( num_projection_layers=1, projection_dims=256, dropout_rate=0.1)dual_encoder = dualencoder(text_encoder, vision_encoder, temperature=0.05)dual_encoder.compile( optimizer=tfa.optimizers.adamw(learning_rate=0.001, weight_decay=0.001))
值得注意的是使用 v100 gpu 加速器训练 60000 个图像标题对的模型，批量大小为 256 个，每个 epoch 需要 12 分钟左右。如果有2个gpu，则每个epoch需要8分钟左右。
print(fnumber of gpus: {len(tf.config.list_physical_devices('gpu'))})print(fnumber of examples (caption-image pairs): {train_example_count})print(fbatch size: {batch_size})print(fsteps per epoch: {int(np.ceil(train_example_count / batch_size))})train_dataset = get_dataset(os.path.join(tfrecords_dir, train-*.tfrecord), batch_size)valid_dataset = get_dataset(os.path.join(tfrecords_dir, valid-*.tfrecord), batch_size)# create a learning rate scheduler callback.reduce_lr = keras.callbacks.reducelronplateau( monitor=val_loss, factor=0.2, patience=3)# create an early stopping callback.early_stopping = tf.keras.callbacks.earlystopping( monitor=val_loss, patience=5, restore_best_weights=true)history = dual_encoder.fit( train_dataset, epochs=num_epochs, validation_data=valid_dataset, callbacks=[reduce_lr, early_stopping],)print(training completed. saving vision and text encoders...)vision_encoder.save(vision_encoder)text_encoder.save(text_encoder)print(models are saved.)
number of gpus: 2number of examples (caption-image pairs): 60000batch size: 256steps per epoch: 235epoch 1/5235/235 [==============================] - 573s 2s/step - loss: 60.8318 - val_loss: 9.0531epoch 2/5235/235 [==============================] - 553s 2s/step - loss: 7.8959 - val_loss: 5.2654epoch 3/5235/235 [==============================] - 541s 2s/step - loss: 4.6644 - val_loss: 4.9260epoch 4/5235/235 [==============================] - 538s 2s/step - loss: 4.0188 - val_loss: 4.6312epoch 5/5235/235 [==============================] - 539s 2s/step - loss: 3.5555 - val_loss: 4.3503training completed. saving vision and text encoders...models are saved. 训练损失的绘制：
plt.plot(history.history[loss])plt.plot(history.history[val_loss])plt.ylabel(loss)plt.xlabel(epoch)plt.legend([train, valid], loc=upper right)plt.show()
9 使用自然语言查询搜索图像
我们可以通过以下步骤来检索对应自然语言查询的图像：
1. 将图像输入vision_encoder，生成图像的嵌入。
2. 将自然语言查询反馈给text_encoder，生成查询嵌入。
3. 计算查询嵌入与索引中的图像嵌入之间的相似度，以检索出最匹配的索引。
4. 查阅顶部匹配图片的路径，将其显示出来。
值得注意的是在训练完双编码器后，将只使用微调后的visual_encoder和text_encoder模型，而dual_encoder模型将被丢弃。
生成图像的嵌入
我们加载图像，并将其输入到vision_encoder中，以生成它们的嵌入。在大规模系统中，这一步是使用并行数据处理框架来执行的，比如apache spark或apache beam。生成图像嵌入可能需要几分钟时间。
print(loading vision and text encoders...)vision_encoder = keras.models.load_model(vision_encoder)text_encoder = keras.models.load_model(text_encoder)print(models are loaded.) def read_image(image_path): image_array = tf.image.decode_jpeg(tf.io.read_file(image_path), channels=3) return tf.image.resize(image_array, (299, 299)) print(fgenerating embeddings for {len(image_paths)} images...)image_embeddings = vision_encoder.predict( tf.data.dataset.from_tensor_slices(image_paths).map(read_image).batch(batch_size), verbose=1,)print(fimage embeddings shape: {image_embeddings.shape}.)
loading vision and text encoders...models are loaded.generating embeddings for 82783 images...324/324 [==============================] - 437s 1s/stepimage embeddings shape: (82783, 256).
检索相关图像
该例子中，我们通过计算输入的查询嵌入和图像嵌入之间的点积相似度来使用精确匹配，并检索前k个匹配。然而，在实时用例中，使用scann、annoy或faiss等框架进行近似匹配是首选，以扩展大量图像。
def find_matches(image_embeddings, queries, k=9, normalize=true): # get the embedding for the query. query_embedding = text_encoder(tf.convert_to_tensor(queries)) # normalize the query and the image embeddings. if normalize: image_embeddings = tf.math.l2_normalize(image_embeddings, axis=1) query_embedding = tf.math.l2_normalize(query_embedding, axis=1) # compute the dot product between the query and the image embeddings. dot_similarity = tf.matmul(query_embedding, image_embeddings, transpose_b=true) # retrieve top k indices. results = tf.math.top_k(dot_similarity, k).indices.numpy() # return matching image paths. return [[image_paths[idx] for idx in indices] for indices in results] 将查询变量设置为你要搜索的图片类型。试试像 一盘健康的食物, 一个戴着帽子的女人走在人行道上, 一只鸟坐在水边, 或 野生动物站在田野里。
query = a family standing next to the ocean on a sandy beach with a surf boardmatches = find_matches(image_embeddings, [query], normalize=true)[0] plt.figure(figsize=(20, 20))for i in range(9): ax = plt.subplot(3, 3, i + 1) plt.imshow(mpimg.imread(matches[i])) plt.axis(off)
评估检索质量
为了评估双编码器模型，我们使用标题作为查询。使用训练外样本图像和标题来评估检索质量，使用top k精度。如果对于一个给定的标题，其相关的图像在前k个匹配范围内被检索到，则算作一个真正的预测。
def compute_top_k_accuracy(image_paths, k=100): hits = 0 num_batches = int(np.ceil(len(image_paths) / batch_size)) for idx in tqdm(range(num_batches)): start_idx = idx * batch_size end_idx = start_idx + batch_size current_image_paths = image_paths[start_idx:end_idx] queries = [ image_path_to_caption[image_path][0] for image_path in current_image_paths ] result = find_matches(image_embeddings, queries, k) hits += sum( [ image_path in matches for (image_path, matches) in list(zip(current_image_paths, result)) ] ) return hits / len(image_paths) print(scoring training data...)train_accuracy = compute_top_k_accuracy(train_image_paths)print(ftrain accuracy: {round(train_accuracy * 100, 3)}%) print(scoring evaluation data...)eval_accuracy = compute_top_k_accuracy(image_paths[train_size:])print(feval accuracy: {round(eval_accuracy * 100, 3)}%)
0%| | 0/118 [00:00 结束语
你可以通过增加训练样本的大小，训练更多的时期，探索其他图像和文本的基础编码器，设置基础编码器的可训练性，以及调整超参数，特别是softmax的temperature loss计算，获得更好的结果。
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1 介绍 该示例演示了如何构建一个双编码器（也称为双塔）神经网络模型，以使用自然语言搜索图像。该模型的灵感来自于alec radford等人提出的clip方法，其思想是联合训练一个视觉编码器和一个文本编码器，将图像及其标题的表示投射到同一个嵌入空间，从而使标题嵌入位于其描述的图像的嵌入附近。 这个例子需要tensorflow 2.4或更高版本。此外，bert模型需要tensorflow hub和tensorflow text，adamw优化器需要tensorflow addons。这些库可以使用以下命令进行安装。
pip install -q -u tensorflow-hub tensorflow-text tensorflow-addons
2 安装
import osimport collectionsimport jsonimport numpy as npimport tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import layersimport tensorflow_hub as hubimport tensorflow_text as textimport tensorflow_addons as tfaimport matplotlib.pyplot as pltimport matplotlib.image as mpimgfrom tqdm import tqdm # suppressing tf.hub warningstf.get_logger().setlevel(error)
3 准备数据
我们使用ms-coco数据集来训练我们的双编码器模型。ms-coco包含超过82,000张图片，每张图片至少有5个不同的标题注释。该数据集通常用image captioning任务，但我们可以重新利用图像标题对来训练双编码器模型进行图像搜索。
下载提取数据
首先，下载数据集，它由两个压缩文件夹组成：一个是图像，另一个是相关的图像标题。值得注意的是压缩后的图像文件夹大小为13gb。
root_dir = datasetsannotations_dir = os.path.join(root_dir, annotations)images_dir = os.path.join(root_dir, train2014)tfrecords_dir = os.path.join(root_dir, tfrecords)annotation_file = os.path.join(annotations_dir, captions_train2014.json) # download caption annotation filesif not os.path.exists(annotations_dir): annotation_zip = tf.keras.utils.get_file( captions.zip, cache_dir=os.path.abspath(.), origin=https://images.cocodataset.org/annotations/annotations_trainval2014.zip, extract=true, ) os.remove(annotation_zip) # download image filesif not os.path.exists(images_dir): image_zip = tf.keras.utils.get_file( train2014.zip, cache_dir=os.path.abspath(.), origin=https://images.cocodataset.org/zips/train2014.zip, extract=true, ) os.remove(image_zip) print(dataset is downloaded and extracted successfully.) with open(annotation_file, r) as f: annotations = json.load(f)[annotations] image_path_to_caption = collections.defaultdict(list)for element in annotations: caption = f{element['caption'].lower().rstrip('.')} image_path = images_dir + /coco_train2014_ + %012d.jpg % (element[image_id]) image_path_to_caption[image_path].append(caption) image_paths = list(image_path_to_caption.keys())print(fnumber of images: {len(image_paths)})
downloading data from https://images.cocodataset.org/annotations/annotations_trainval2014.zip252878848/252872794 [==============================] - 5s 0us/stepdownloading data from https://images.cocodataset.org/zips/train2014.zip13510574080/13510573713 [==============================] - 394s 0us/stepdataset is downloaded and extracted successfully.number of images: 82783
处理并将数据保存到tfrecord文件中
你可以改变sample_size参数去控制将用于训练双编码器模型的多对图像-标题。在这个例子中，我们将training_size设置为30000张图像，约占数据集的35%。我们为每张图像使用2个标题，从而产生60000个图像-标题对。训练集的大小会影响生成编码器的质量，样本越多，训练时间越长。
train_size = 30000valid_size = 5000captions_per_image = 2images_per_file = 2000train_image_paths = image_paths[:train_size]num_train_files = int(np.ceil(train_size / images_per_file))train_files_prefix = os.path.join(tfrecords_dir, train) valid_image_paths = image_paths[-valid_size:]num_valid_files = int(np.ceil(valid_size / images_per_file))valid_files_prefix = os.path.join(tfrecords_dir, valid) tf.io.gfile.makedirs(tfrecords_dir) def bytes_feature(value): return tf.train.feature(bytes_list=tf.train.byteslist(value=[value])) def create_example(image_path, caption): feature = { caption: bytes_feature(caption.encode()), raw_image: bytes_feature(tf.io.read_file(image_path).numpy()), } return tf.train.example(features=tf.train.features(feature=feature)) def write_tfrecords(file_name, image_paths): caption_list = [] image_path_list = [] for image_path in image_paths: captions = image_path_to_caption[image_path][:captions_per_image] caption_list.extend(captions) image_path_list.extend([image_path] * len(captions)) with tf.io.tfrecordwriter(file_name) as writer: for example_idx in range(len(image_path_list)): example = create_example( image_path_list[example_idx], caption_list[example_idx] ) writer.write(example.serializetostring()) return example_idx + 1 def write_data(image_paths, num_files, files_prefix): example_counter = 0 for file_idx in tqdm(range(num_files)): file_name = files_prefix + -%02d.tfrecord % (file_idx) start_idx = images_per_file * file_idx end_idx = start_idx + images_per_file example_counter += write_tfrecords(file_name, image_paths[start_idx:end_idx]) return example_counter train_example_count = write_data(train_image_paths, num_train_files, train_files_prefix)print(f{train_example_count} training examples were written to tfrecord files.) valid_example_count = write_data(valid_image_paths, num_valid_files, valid_files_prefix)print(f{valid_example_count} evaluation examples were written to tfrecord files.)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [03:19<00:00, 13.27s/it] 0%| | 0/3 [00:00 创建用于训练和评估的 tf.data.dataset
feature_description = { caption: tf.io.fixedlenfeature([], tf.string), raw_image: tf.io.fixedlenfeature([], tf.string),} def read_example(example): features = tf.io.parse_single_example(example, feature_description) raw_image = features.pop(raw_image) features[image] = tf.image.resize( tf.image.decode_jpeg(raw_image, channels=3), size=(299, 299) ) return features def get_dataset(file_pattern, batch_size): return ( tf.data.tfrecorddataset(tf.data.dataset.list_files(file_pattern)) .map( read_example, num_parallel_calls=tf.data.experimental.autotune, deterministic=false, ) .shuffle(batch_size * 10) .prefetch(buffer_size=tf.data.experimental.autotune) .batch(batch_size) ) 4 实时投影头
投影头用于将图像和文字嵌入到具有相同的维度的同一嵌入空间。
def project_embeddings( embeddings, num_projection_layers, projection_dims, dropout_rate): projected_embeddings = layers.dense(units=projection_dims)(embeddings) for _ in range(num_projection_layers): x = tf.nn.gelu(projected_embeddings) x = layers.dense(projection_dims)(x) x = layers.dropout(dropout_rate)(x) x = layers.add()([projected_embeddings, x]) projected_embeddings = layers.layernormalization()(x) return projected_embeddings 5 实现视觉编码器
在本例中，我们使用keras applications的xception作为视觉编码器的基础。
def create_vision_encoder( num_projection_layers, projection_dims, dropout_rate, trainable=false): # load the pre-trained xception model to be used as the base encoder. xception = keras.applications.xception( include_top=false, weights=imagenet, pooling=avg ) # set the trainability of the base encoder. for layer in xception.layers: layer.trainable = trainable # receive the images as inputs. inputs = layers.input(shape=(299, 299, 3), name=image_input) # preprocess the input image. xception_input = tf.keras.applications.xception.preprocess_input(inputs) # generate the embeddings for the images using the xception model. embeddings = xception(xception_input) # project the embeddings produced by the model. outputs = project_embeddings( embeddings, num_projection_layers, projection_dims, dropout_rate ) # create the vision encoder model. return keras.model(inputs, outputs, name=vision_encoder) 6 实现文本编码器 我们使用tensorflow hub的bert作为文本编码器
def create_text_encoder( num_projection_layers, projection_dims, dropout_rate, trainable=false): # load the bert preprocessing module. preprocess = hub.keraslayer( https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/2, name=text_preprocessing, ) # load the pre-trained bert model to be used as the base encoder. bert = hub.keraslayer( https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_l-4_h-512_a-8/1, bert, ) # set the trainability of the base encoder. bert.trainable = trainable # receive the text as inputs. inputs = layers.input(shape=(), dtype=tf.string, name=text_input) # preprocess the text. bert_inputs = preprocess(inputs) # generate embeddings for the preprocessed text using the bert model. embeddings = bert(bert_inputs)[pooled_output] # project the embeddings produced by the model. outputs = project_embeddings( embeddings, num_projection_layers, projection_dims, dropout_rate ) # create the text encoder model. return keras.model(inputs, outputs, name=text_encoder)
7 实现双编码器
为了计算loss，我们计算每个 caption_i和 images_j之间的对偶点积相似度作为预测值。caption_i和image_j之间的目标相似度计算为（caption_i和caption_j之间的点积相似度）和（image_i和image_j之间的点积相似度）的平均值。然后，我们使用交叉熵来计算目标和预测之间的损失。
class dualencoder(keras.model): def __init__(self, text_encoder, image_encoder, temperature=1.0, **kwargs): super(dualencoder, self).__init__(**kwargs) self.text_encoder = text_encoder self.image_encoder = image_encoder self.temperature = temperature self.loss_tracker = keras.metrics.mean(name=loss) @property def metrics(self): return [self.loss_tracker] def call(self, features, training=false): # place each encoder on a separate gpu (if available). # tf will fallback on available devices if there are fewer than 2 gpus. with tf.device(/gpu:0): # get the embeddings for the captions. caption_embeddings = text_encoder(features[caption], training=training) with tf.device(/gpu:1): # get the embeddings for the images. image_embeddings = vision_encoder(features[image], training=training) return caption_embeddings, image_embeddings def compute_loss(self, caption_embeddings, image_embeddings): # logits[i][j] is the dot_similarity(caption_i, image_j). logits = ( tf.matmul(caption_embeddings, image_embeddings, transpose_b=true) / self.temperature ) # images_similarity[i][j] is the dot_similarity(image_i, image_j). images_similarity = tf.matmul( image_embeddings, image_embeddings, transpose_b=true ) # captions_similarity[i][j] is the dot_similarity(caption_i, caption_j). captions_similarity = tf.matmul( caption_embeddings, caption_embeddings, transpose_b=true ) # targets[i][j] = avarage dot_similarity(caption_i, caption_j) and dot_similarity(image_i, image_j). targets = keras.activations.softmax( (captions_similarity + images_similarity) / (2 * self.temperature) ) # compute the loss for the captions using crossentropy captions_loss = keras.losses.categorical_crossentropy( y_true=targets, y_pred=logits, from_logits=true ) # compute the loss for the images using crossentropy images_loss = keras.losses.categorical_crossentropy( y_true=tf.transpose(targets), y_pred=tf.transpose(logits), from_logits=true ) # return the mean of the loss over the batch. return (captions_loss + images_loss) / 2 def train_step(self, features): with tf.gradienttape() as tape: # forward pass caption_embeddings, image_embeddings = self(features, training=true) loss = self.compute_loss(caption_embeddings, image_embeddings) # backward pass gradients = tape.gradient(loss, self.trainable_variables) self.optimizer.apply_gradients(zip(gradients, self.trainable_variables)) # monitor loss self.loss_tracker.update_state(loss) return {loss: self.loss_tracker.result()} def test_step(self, features): caption_embeddings, image_embeddings = self(features, training=false) loss = self.compute_loss(caption_embeddings, image_embeddings) self.loss_tracker.update_state(loss) return {loss: self.loss_tracker.result()}
8 训练双编码模型
在这个实验中，我们冻结了文字和图像的基础编码器，只让投影头进行训练。
num_epochs = 5 # in practice, train for at least 30 epochsbatch_size = 256 vision_encoder = create_vision_encoder( num_projection_layers=1, projection_dims=256, dropout_rate=0.1)text_encoder = create_text_encoder( num_projection_layers=1, projection_dims=256, dropout_rate=0.1)dual_encoder = dualencoder(text_encoder, vision_encoder, temperature=0.05)dual_encoder.compile( optimizer=tfa.optimizers.adamw(learning_rate=0.001, weight_decay=0.001))
值得注意的是使用 v100 gpu 加速器训练 60000 个图像标题对的模型，批量大小为 256 个，每个 epoch 需要 12 分钟左右。如果有2个gpu，则每个epoch需要8分钟左右。
print(fnumber of gpus: {len(tf.config.list_physical_devices('gpu'))})print(fnumber of examples (caption-image pairs): {train_example_count})print(fbatch size: {batch_size})print(fsteps per epoch: {int(np.ceil(train_example_count / batch_size))})train_dataset = get_dataset(os.path.join(tfrecords_dir, train-*.tfrecord), batch_size)valid_dataset = get_dataset(os.path.join(tfrecords_dir, valid-*.tfrecord), batch_size)# create a learning rate scheduler callback.reduce_lr = keras.callbacks.reducelronplateau( monitor=val_loss, factor=0.2, patience=3)# create an early stopping callback.early_stopping = tf.keras.callbacks.earlystopping( monitor=val_loss, patience=5, restore_best_weights=true)history = dual_encoder.fit( train_dataset, epochs=num_epochs, validation_data=valid_dataset, callbacks=[reduce_lr, early_stopping],)print(training completed. saving vision and text encoders...)vision_encoder.save(vision_encoder)text_encoder.save(text_encoder)print(models are saved.)
number of gpus: 2number of examples (caption-image pairs): 60000batch size: 256steps per epoch: 235epoch 1/5235/235 [==============================] - 573s 2s/step - loss: 60.8318 - val_loss: 9.0531epoch 2/5235/235 [==============================] - 553s 2s/step - loss: 7.8959 - val_loss: 5.2654epoch 3/5235/235 [==============================] - 541s 2s/step - loss: 4.6644 - val_loss: 4.9260epoch 4/5235/235 [==============================] - 538s 2s/step - loss: 4.0188 - val_loss: 4.6312epoch 5/5235/235 [==============================] - 539s 2s/step - loss: 3.5555 - val_loss: 4.3503training completed. saving vision and text encoders...models are saved. 训练损失的绘制：
plt.plot(history.history[loss])plt.plot(history.history[val_loss])plt.ylabel(loss)plt.xlabel(epoch)plt.legend([train, valid], loc=upper right)plt.show()
9 使用自然语言查询搜索图像
我们可以通过以下步骤来检索对应自然语言查询的图像：
1. 将图像输入vision_encoder，生成图像的嵌入。
2. 将自然语言查询反馈给text_encoder，生成查询嵌入。
3. 计算查询嵌入与索引中的图像嵌入之间的相似度，以检索出最匹配的索引。
4. 查阅顶部匹配图片的路径，将其显示出来。
值得注意的是在训练完双编码器后，将只使用微调后的visual_encoder和text_encoder模型，而dual_encoder模型将被丢弃。
生成图像的嵌入
我们加载图像，并将其输入到vision_encoder中，以生成它们的嵌入。在大规模系统中，这一步是使用并行数据处理框架来执行的，比如apache spark或apache beam。生成图像嵌入可能需要几分钟时间。
print(loading vision and text encoders...)vision_encoder = keras.models.load_model(vision_encoder)text_encoder = keras.models.load_model(text_encoder)print(models are loaded.) def read_image(image_path): image_array = tf.image.decode_jpeg(tf.io.read_file(image_path), channels=3) return tf.image.resize(image_array, (299, 299)) print(fgenerating embeddings for {len(image_paths)} images...)image_embeddings = vision_encoder.predict( tf.data.dataset.from_tensor_slices(image_paths).map(read_image).batch(batch_size), verbose=1,)print(fimage embeddings shape: {image_embeddings.shape}.)
loading vision and text encoders...models are loaded.generating embeddings for 82783 images...324/324 [==============================] - 437s 1s/stepimage embeddings shape: (82783, 256).
检索相关图像
该例子中，我们通过计算输入的查询嵌入和图像嵌入之间的点积相似度来使用精确匹配，并检索前k个匹配。然而，在实时用例中，使用scann、annoy或faiss等框架进行近似匹配是首选，以扩展大量图像。
def find_matches(image_embeddings, queries, k=9, normalize=true): # get the embedding for the query. query_embedding = text_encoder(tf.convert_to_tensor(queries)) # normalize the query and the image embeddings. if normalize: image_embeddings = tf.math.l2_normalize(image_embeddings, axis=1) query_embedding = tf.math.l2_normalize(query_embedding, axis=1) # compute the dot product between the query and the image embeddings. dot_similarity = tf.matmul(query_embedding, image_embeddings, transpose_b=true) # retrieve top k indices. results = tf.math.top_k(dot_similarity, k).indices.numpy() # return matching image paths. return [[image_paths[idx] for idx in indices] for indices in results] 将查询变量设置为你要搜索的图片类型。试试像 一盘健康的食物, 一个戴着帽子的女人走在人行道上, 一只鸟坐在水边, 或 野生动物站在田野里。
query = a family standing next to the ocean on a sandy beach with a surf boardmatches = find_matches(image_embeddings, [query], normalize=true)[0] plt.figure(figsize=(20, 20))for i in range(9): ax = plt.subplot(3, 3, i + 1) plt.imshow(mpimg.imread(matches[i])) plt.axis(off)
评估检索质量
为了评估双编码器模型，我们使用标题作为查询。使用训练外样本图像和标题来评估检索质量，使用top k精度。如果对于一个给定的标题，其相关的图像在前k个匹配范围内被检索到，则算作一个真正的预测。
def compute_top_k_accuracy(image_paths, k=100): hits = 0 num_batches = int(np.ceil(len(image_paths) / batch_size)) for idx in tqdm(range(num_batches)): start_idx = idx * batch_size end_idx = start_idx + batch_size current_image_paths = image_paths[start_idx:end_idx] queries = [ image_path_to_caption[image_path][0] for image_path in current_image_paths ] result = find_matches(image_embeddings, queries, k) hits += sum( [ image_path in matches for (image_path, matches) in list(zip(current_image_paths, result)) ] ) return hits / len(image_paths) print(scoring training data...)train_accuracy = compute_top_k_accuracy(train_image_paths)print(ftrain accuracy: {round(train_accuracy * 100, 3)}%) print(scoring evaluation data...)eval_accuracy = compute_top_k_accuracy(image_paths[train_size:])print(feval accuracy: {round(eval_accuracy * 100, 3)}%)
0%| | 0/118 [00:00 结束语
你可以通过增加训练样本的大小，训练更多的时期，探索其他图像和文本的基础编码器，设置基础编码器的可训练性，以及调整超参数，特别是softmax的temperature loss计算，获得更好的结果。
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